Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Mar 4;21(3):242.
doi: 10.3390/e21030242.

A Method Based on Differential Entropy-Like Function for Detecting Differentially Expressed Genes Across Multiple Conditions in RNA-Seq Studies

Zhuo Wang  1 , Shuilin Jin  1 , Chiping Zhang  1
Affiliations

A Method Based on Differential Entropy-Like Function for Detecting Differentially Expressed Genes Across Multiple Conditions in RNA-Seq Studies

Zhuo Wang et al. Entropy (Basel). .

Abstract

The advancement of high-throughput RNA sequencing has uncovered the profound truth in biology, ranging from the study of differential expressed genes to the identification of different genomic phenotype across multiple conditions. However, lack of biological replicates and low expressed data are still obstacles to measuring differentially expressed genes effectively. We present an algorithm based on differential entropy-like function (DEF) to test for the differential expression across time-course data or multi-sample data with few biological replicates. Compared with limma, edgeR, DESeq2, and baySeq, DEF maintains equivalent or better performance on the real data of two conditions. Moreover, DEF is well suited for predicting the genes that show the greatest differences across multiple conditions such as time-course data and identifies various biologically relevant genes.

Keywords: differential entropy-like function; differential expressed genes; multiple condition data; time-course data.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Venn diagram of differentially expressed genes obtained from limma, baySeq, DESeq2, edgeR and DEF: (a) read counts from “Sultan” dataset with the threshold for DEF entropy-like value of 0.05; and (b) read counts from “Sultan” dataset with the threshold for DEF entropy-like value of 0.01.
Figure 2
Figure 2
Venn diagram of differentially expressed genes obtained from limma, baySeq, DESeq2, edgeR and DEF: (a) read counts from “Katz” dataset with the threshold for DEF value of 0.05; and (b) read counts from “Katz” dataset with the threshold for DEF value of 0.01.
Figure 3
Figure 3
The performance of DEF on the two-group data: (a) box plots of the top 100 differentially expressed genes from “Sultan” dataset; and (b) box plots of the top 100 differentially expressed genes from “Katz” dataset.
Figure 4
Figure 4
The performance of DEF on time-course data: (a) normalized read counts of the top five differentially expressed genes over four time points; and (b) box plots of the top 100 differentially expressed genes on the first time point compared with every time point.
Figure 5
Figure 5
Box plot of the top 100 differentially expressed genes and last 100 non DE genes of 41 samples separately.
Figure 6
Figure 6
Box plot of the top 100 differentially expressed of nine tissues separately.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Wang Z., Gerstein M., Snyder M. RNA-Seq: A revolutionary tool for transcriptomics. Nat. Rev. Genet. 2009;10:57–63. doi: 10.1038/nrg2484. - DOI - PMC - PubMed
    1. Trapnell C., Williams B.A., Pertea G., Mortazavi A., Kwan G., van Baren M.J., Salzberg S.L., Wold B.J., Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 2010;28:511–515. doi: 10.1038/nbt.1621. - DOI - PMC - PubMed
    1. Clavijo B.J., Venturini L., Schudoma C., Accinelli G.G., Kaithakottil G., Wright J., Borrill P., Kettleborough G., Heavens D., Chapman H., et al. An improved assembly and annotation of the allohexaploid wheat genome identifies complete families of agronomic genes and provides genomic evidence for chromosomal translocations. Genome Res. 2017;27:885–896. doi: 10.1101/gr.217117.116. - DOI - PMC - PubMed
    1. Chepelev I., Wei G., Tang Q., Zhao K. Detection of single nucleotide variations in expressed exons of the human genome using RNA-Seq. Nucleic Acids Res. 2009;37:e106. doi: 10.1093/nar/gkp507. - DOI - PMC - PubMed
    1. Velculescu V.E., Zhang L., Vogelstein B., Kinzler K.W. Serial Analysis of Gene Expression. Science. 1995;270:484–487. doi: 10.1126/science.270.5235.484. - DOI - PubMed

LinkOut - more resources